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MCP_Based_AI_Assistant

MCP_Based_AI_Assistant

by Archana-M-11
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

MCP_Based_AI_Assistant

An AI assistant built using Model Context Protocol (MCP) and LangGraph. The assistant can understand user queries, determine whether a tool is required, and invoke the appropriate MCP tool through an MCP client.

  • MCP server with multiple tools

  • MCP client for connecting to the MCP server

  • MCP tool discovery

  • LangGraph-based stateful agent workflow

  • LLM-based tool selection

  • Conditional tool execution

  • Human-in-the-Loop approval for sensitive tools

  • Document search

  • Employee information lookup

  • Mathematical expression calculation

  • LLM-based final response generation

  • Test cases for validating different queries and tool workflows

Architecture

Architecture Diagram

Related MCP server: wissens-enablement-agent

Installation

1. Clone the repository

git clone <your-repository-url>
cd MCP_Based_AI_Assistant

2. Create a virtual environment

uv venv

3. Activate the virtual environment

Windows PowerShell

.venv\Scripts\Activate.ps1

4. Install dependencies

uv sync

5. Configure environment variables

Create a .env file in the project root directory:

GEMINI_API_KEY=your_gemini_api_key

6. Run the test cases

uv run python -m tests.test

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    This server cannot be deployed

    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    Maintenance

    ActivityStale
    ResponsivenessNo recent community issues

    Resources

    Related MCP Connectors

    Related MCP Servers

    • wissens-enablement-agent

      denny98-lab
      F
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      Not graded
      quality
      C
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      Enables AI assistants to securely search and list internal company documents via MCP tools, so they can answer employee questions based on current internal documentation.
      -

    • IntelliDocs AI

      rupali-chauksey
      A
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      Enables a unified AI assistant that combines document retrieval (RAG), database queries via MCP tools, and web search, allowing users to ask complex questions and receive answers from multiple enterprise sources.
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    • agent-gateway

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      Enables AI agents to discover and query enterprise knowledge documents and business metrics through standardized MCP tools, supporting retrieval, structured sales/order analysis, anomaly scanning, and data lineage lookups.
      12
      -